/*
 * Copyright (c) 2025 Huawei Technologies Co., Ltd.
 * This program is free software, you can redistribute it and/or modify it under the terms and conditions of
 * CANN Open Software License Agreement Version 2.0 (the "License").
 * Please refer to the License for details. You may not use this file except in compliance with the License.
 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
 * INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
 * See LICENSE in the root of the software repository for the full text of the License.
 */

#include "ascir/Dialect/Asc/IR/Asc.h"
#include "ascir/Dialect/Asc/Transforms/Passes.h"
#include "ascir/Dialect/Utils/ConstantOpBuilder.h"

#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/IR/ValueRange.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"

namespace mlir {
namespace ascendc {
#define GEN_PASS_DEF_INPUTOUTPUTTENSOR
#include "ascir/Dialect/Asc/Transforms/Passes.h.inc"
} // namespace ascendc
} // namespace mlir

using namespace mlir;
using namespace mlir::ascendc;

namespace {

void createDataCopyIfNeeded(Operation *op) {
  for (auto &use : op->getUses()) {
    auto copyOp = dyn_cast<ascendc::DataCopyOp>(use.getOwner());
    if (!copyOp || copyOp.getDirection() != ascendc::CopyDirection::ubuf_gm)
        return;
    OpBuilder builder(op);
    ascir::ConstantOpBuilder consts(builder);
    auto type = cast<ascendc::BaseTensorType>(use.get().getType());
    auto dst = builder.create<ascendc::LocalTensorAutoOp>(
        op->getLoc(), type, /*input*/ false,
        /*output*/ true, ValueRange{});
    builder.setInsertionPointAfter(op);
    Value calCount = consts.i64(type.getNumElements());
    builder.create<ascendc::DataCopyL2Op>(op->getLoc(), dst, use.get(),
                                          calCount);
    copyOp.setSrc(dst);
  }
}

void setInOutTensors(func::FuncOp funcOp) {
  funcOp.walk([](ascendc::LocalTensorAutoOp op) {
    bool input = false;
    bool output = false;
    for (Operation *user : op->getUsers()) {
        if (auto copyOp = dyn_cast<ascendc::DataCopyOp>(user)) {
            auto dir = copyOp.getDirection();
            if (dir == ascendc::CopyDirection::gm_ubuf) {
                input = true;
                continue;
            }
            if (dir == ascendc::CopyDirection::ubuf_gm) {
                output = true;
                continue;
            }
        }
    }
    op.setInput(input);
    op.setOutput(output);
  });
  funcOp.walk([](scf::ForOp op) { createDataCopyIfNeeded(op); });
  funcOp.walk([](scf::IfOp op) { createDataCopyIfNeeded(op); });
}

void fixInOutTensor(func::FuncOp &funcOp) {
  funcOp.walk([](ascendc::LocalTensorAutoOp inTensor) {
    if (!inTensor.getInput() || inTensor.getOutput())
        return;
    auto loc = inTensor.getLoc();
    OpBuilder builder(inTensor);
    auto tensorType = inTensor.getResult().getType();
    inTensor.setOutput(false);
    for (auto &use : inTensor->getUses()) {
        auto *owner = use.getOwner();
        auto copyOp = dyn_cast<ascendc::DataCopyOp>(owner);
        if (!copyOp || copyOp.getDirection() != ascendc::CopyDirection::ubuf_gm)
            return builder.setInsertionPoint(owner);
        ascir::ConstantOpBuilder consts(builder);
        Value calCount = consts.i64(tensorType.getNumElements());
        auto outTensor = builder.create<ascendc::LocalTensorAutoOp>(
            loc, tensorType, /*input*/ false, /*output*/ true, ValueRange{});
        builder.create<ascendc::DataCopyL2Op>(loc, outTensor, inTensor, calCount);
        owner->setOperand(use.getOperandNumber(), outTensor);
    }
  });
}

struct InputOutputTensorPass
    : public ascendc::impl::InputOutputTensorBase<InputOutputTensorPass> {
  void runOnOperation() override {
    func::FuncOp funcOp = getOperation();
    if (funcOp.isDeclaration()) {
        return;
    }
    setInOutTensors(funcOp);
    fixInOutTensor(funcOp);
    MLIRContext *context = &getContext();
    RewritePatternSet patterns(context);
    ascendc::LocalTensorAutoOp::getCanonicalizationPatterns(patterns, context);
    if (applyPatternsAndFoldGreedily(funcOp, std::move(patterns)).failed()) {
        signalPassFailure();
        return;
    }
  }
};

}  // namespace

namespace mlir {
namespace ascendc {
std::unique_ptr<Pass> createInputOutputTensorPass() {
  return std::make_unique<InputOutputTensorPass>();
}
} // namespace ascendc
} // namespace mlir
